A Blockchain-based Co-Simulation Platform for Transparent and Fair Energy Trading and Management
Bibliographic record
Abstract
With the rapid adoption of renewable energy and smart meters, more distributed energy consumers are becoming capable of generating energy and participating in energy trading. However, it raises great challenges to establish trust among these distributed energy sources. A more transparent and fair energy trading market is required. With its unique advantages in supporting fair and transparent transactions, blockchain is recognized as an effective solution to facilitate distributed energy transactions. However, existing studies often propose blockchain-based energy trading schemes without considering the management of energy generation, consumption, and transmission. In addition, the sensitive nature of the power grid may make the grid operators hesitate to adopt anyone to directly access the energy trading market. Therefore, in this study, we adopt a permission-based blockchain, Hyper-ledger Fabric, to establish a fair and transparent distributed energy trading market, due to its strong access control and efficient consensus mechanism. Furthermore, the proposed blockchain-based energy trading market is integrated with the Packetized Energy Management and Trading Co-Simulation platform (PEMT-CoSim), developed by our prior work, so that a holistic co-simulation platform is established to facilitate further studies by closely coordinating energy trading and management. The demonstration results based on the proposed blockchain-based co-simulation platform are discussed in detail, which validate the effectiveness of the proposed architecture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".